AIGC adds infinite possibilities to Web3.
Original: Crypto.com, compiled by The Way of DeFi.
AI has leapt to the next level and is now helping to build Web3. This article will help you understand how generative AI will shape the future of Web3.
Main points of this article:
- Generative AI is a type of AI used to generate artificial content such as text, images, audio, and video content.
- AI applications in Web3 include deploying Crypto collectibles in games, NFTs, asset creation, and software development.
- Beyond content generation, AI can help advance Web3 by simplifying the development process and improving the user experience of decentralized applications (dapps).
- While challenges such as copyright, accuracy, and creativity remain, the age of AI has arrived—AI models of all kinds are changing the way businesses and industries operate.
AI Generated Content (AIGC) – The Next Phase of Content Generation
AI-generated content (AIGC) has become very popular recently, with apps like DALL-E and ChatGPT producing impressive visual assets and enabling human-like conversations.
Broadly speaking, generative AI is a type of AI used to generate content (such as text, images, audio, and video) through computer models. AIGC is widely considered to be the next stage of content generation after Professionally Generated Content (PGC) and User Generated Content (UGC).
PGCs are typically produced by creative professionals such as graphic designers and animators for use or distribution by brands, while UGCs are created by end users and shared directly on social media sites such as YouTube, Facebook or Twitter.
In recent years, with the rapid development of AI, it can generate various types of content. Some related branches of AI are natural language processing (NLP), which studies how computers process and analyze text, and generative adversarial networks (GAN), which aim to generate new data (such as images and videos) with similar characteristics to the training data set. ).
AI-generated content helps speed up the creative process, and businesses are starting to notice its potential to change the way content is created and how creative teams operate across industries.
Below are potential scenarios and use cases for connecting AI and Web3.
Application of AIGC in Web3
Text-Based AI and Its Impact on Web3
Text-based AI refers to the use of AI to generate text. It is a form of NLP that generates human-like text from a given input and is used in various applications such as summarization, dialogue systems, and machine translation. Today’s text generators are used to generate original, creative content for a variety of purposes, and there are areas in Web3 where text generation could be very useful.
Online search can be reimagined and web navigation more intuitive with text AI tools. ChatGPT’s latest integration with Bing, Microsoft’s online search engine, now introduces a chat interface as a way to search the web.
At the same time, Google released its own version of the NLP model Bard, an experimental conversational AI text service powered by LaMDA that helps simplify complex topics and synthesize insights from queries.
Generative AI could change the way people search the web
Generative AI has the potential to change the way people filter information on the web and has the potential to reduce reliance on the search engine advertising model – something many current Web2 users have long wanted to avoid.
Text generation tools allow users to cut through the noise of SEO-generated content (albeit involving human intervention and fine-tuning) when making queries. If search preferences change in favor of text-based AI tools, search engines could be replaced, meaning less search-related ad clutter to dig into — a core Web3 standard designed to bring the power of technology to back to the user.
In blockchain games, textual AI can enhance the creativity and productivity of game developers and artists in several ways. By leveraging text-based AI, basic video game elements such as dialogue, story, and character composition can be produced and refined quickly, simplifying the creative process by generating ideas faster.
Generative AI could change the way NFTs are generated
AI can also help generate images and videos — these types of content can be minted into NFTs. These artificial intelligence-generated NFTs are called generative art NFTs, and the artist will first enter a set of rules (such as a series of colors and patterns), as well as parameters such as the number of iterations and the degree of randomness. The computer will then generate the artwork within this specified frame.
One such example is “CryptoPunks” generator Larva Labs, which created the “Autoglyphs” NFT collection. Here are other examples of collections of NFTs generated with the help of AI.
Here are some examples of generative art NFTs:
Autoglyphs was released by Larva Labs, the creator of CryptoPunks, built on the Ethereum blockchain, with a total of 512.
Created by visual artist Tyler Hobbs, the Fidenza series utilizes a general algorithm for generating various curves and blocks, totaling 999.
4. Chromie Squiggle
Created by Erick ‘Snowfro’ Calderon, the collection consists of randomly generated squiggles in nine different style schemes, totaling 10,000.
5. Lost Poets
Created by Crypto artist Pak, the series is both an NFT collection and a strategy game, with a total of 65,536 pieces.
AI can help generate avatars and items in blockchain games
Generative AI models can assist in the large-scale creation of game assets—from avatars, equipment, vehicles, to artifacts—in Web3 environments. The gaming industry can apply text-to-image generative AI models capable of generating creative assets and content based on textual descriptions. Within certain parameters, modern language models can also be used to build context around created assets, such as item power stats, character attributes or intelligence.
AI-generated images and videos are now so advanced that they can even be used to create special effects in blockchain games and virtual products in the Metaverse. For example, Mirror World is a GameFi project that utilizes AI-powered virtual “mirrors” as assets for game characters. Mirror assets are fully interoperable within each game, ensuring asset holders can use them to tackle new challenges as games go live.
Alethea AI’s CharacterGPT project is another example of generative AI at work. It features a multimodal AI system called CharacterGPT that can generate interactive AI characters from textual descriptions, enabling text-to-character creation. Based on different natural language descriptions, interactive characters can have different appearances, voices, personalities, and identities.
These characters can be tokenized on the blockchain, and their owners can also customize their personalities and train their intelligence, as well as trade and use them on various other dapps on Alethea’s AI protocol. Proposed use cases for these interactive characters include Crypto twins (virtual models designed to mirror physical objects), Crypto guides, Crypto companions, virtual assistants, and AI non-player characters (NPCs).
AI can help find bugs
AI can help simplify the development process when building Web3 infrastructure and applications.
For example, AI applications are used to debug code. Using AI, ChatGPT has somehow demonstrated the ability not only to read and write code, but also to spot bugs in code.
Some crypto professionals are already using AI programs for simple code auditing tasks: developers at smart contract auditing firm Certik use ChatGPT to “quickly understand and summarize the semantics of complex code snippets.”
Finally: Challenges, Risks and Prospects of AI Use in Web3
The possibilities are endless with AI, its only limit is the user’s imagination. Even at this early stage, AI models continue to demonstrate their ability to transform businesses and even industries. With widespread adoption driven by low barriers to entry, AI is likely to become our future way of life in this Crypto world. However, there are some challenges and risks associated with such technologies.
One of the challenges may be consumer and organizational resistance to AI-generated content. For example, major stock photo site and platform Getty Images prohibits the uploading and selling of illustrations generated using AI art tools. Copyright issues are believed to be the cause, as some AI-generated images reproduce copyrighted content, with the original artist’s watermark still visible.
Another challenge facing the AIGC is the quality of the generated content. Stanford professor Andrew Ng gave an example where ChatGPT wrongly believed how an abacus was faster than a GPU, which thankfully was not the case.
For most in the field, AI is a technology already proven to be disrupting the workforce. However, it is a misconception to think that AI will replace humans at work. In fact, it could actually create new opportunities in both existing and emerging markets: AI will likely help increase job creation, or create new types of AI-related jobs that just require some upskilling.
The future of AI is perhaps best described by a quote from author William Gibson: “The future is already here—it’s just not evenly distributed.” The same can be said about the intersection between AI and Web3 today.
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